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Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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Correction: Rao et al. Ensemble Deep-Learning-Based Prognostic and Prediction for Recurrence of Sporadic Odontogenic Keratocysts on Hematoxylin and Eosin Stained Pathological Images of Incisional Biopsies. <i>J. Pers. Med.</i> 2022, <i>12</i>, 1220.

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Updated: Sep 13, 2025

Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

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Quantitative Method for Monitoring Tumor Evolution During and After Therapy.

Paolo Castorina^1,2,3, Filippo Castiglione^4,5, Gianluca Ferini^6,7

¹Istituto Nazionale Fisica Nucleare, Sezione di Catania, 95123 Catania, Italy.

Journal of Personalized Medicine

|July 25, 2025

Summary

This summary is machine-generated.

This study introduces a computational model to track tumor growth during cancer therapy, aiding personalized treatment decisions. It identifies key dose thresholds for predicting treatment success or tumor regrowth in real-time.

Keywords:

monitoring treatment response predictive personalized tumor progression support clinical decision-making tumor growth

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Last Updated: Sep 13, 2025

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Area of Science:

Oncology
Computational Biology
Medical Physics

Background:

Quantitative analysis of tumor progression during and after therapy is crucial for understanding disease dynamics and treatment effectiveness.
Personalized patient care requires advanced computational tools to model therapy effects.
The Gompertz law provides a framework for modeling tumor growth dynamics.

Purpose of the Study:

To develop and validate a computational approach for quantitative monitoring of tumor progression during therapeutic interventions.
To support clinical decision-making by modeling therapy effects using the Gompertz law.
To create a user-friendly, phenomenological model for personalized patient care.

Main Methods:

Utilized a computational approach based on the Gompertz law to model tumor growth.
Applied the model to data from in vivo studies involving neoadjuvant chemoradiotherapy.
Included analyses of conventional and FLASH radiation treatments.

Main Results:

The model effectively captures distinct phases of tumor response to therapy.
A critical dose threshold was identified that differentiates complete response from partial response or tumor regrowth.
Demonstrated the model's applicability to various therapeutic interventions.

Conclusions:

The developed phenomenological model offers a user-friendly method for real-time quantitative monitoring of disease progression.
Findings support the development of more tailored and predictive clinical strategies for cancer treatment.
Identified a key dose threshold for predicting treatment outcomes, enhancing clinical decision-making.